Methods and Apparatus for Determining Whether a Media Presentation Device is in an On State or an Off State

ABSTRACT

Methods and apparatus for determining whether a media presentation device is in an on state or an off state are disclosed. A disclosed example method comprises determining first and second characteristics of a signature associated with a signal representative of media content presented via a media presentation device, evaluating the first and second characteristics to determine first and second fuzzy contribution values representing, respectively, degrees with which the first and second characteristics correspond to the media presentation device being in at least one of an on state or an off state, determining a third fuzzy contribution value based on a number of the first and second contribution values indicating the media presentation device is in one of the on or off states, and combining the first, second and third fuzzy contribution values for use in determining whether the media presentation device is in the on state or the off state.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to audience measurement, andmore particularly, to methods and apparatus for determining whether amedia presentation device is in an on state or an off state.

BACKGROUND

Media ratings and other audience metering information are typicallygenerated by collecting media exposure information from a group ofstatistically selected households. Each of the statistically selectedhouseholds typically has a data logging and processing unit commonlyreferred to as a “home unit,” “meter” or “audience measurement device.”In metered households or, more generally, metering sites having multiplemedia presentation devices, the data logging and processingfunctionality may be distributed among a single home unit and multiplesite units, where one site unit may be provided for each mediapresentation device or media presentation area. The home unit (or thecombination of the home unit and the site units) includes sensors togather data from the monitored media presentation devices (e.g.,audio-video (AV) devices) at the selected site.

Modern media presentation devices are becoming more complex infunctionality and interoperability with other media presentationdevices. As a result, manufacturers are exploring new, user-friendlyways of standardizing interfaces to simplify the set-up and operation ofthese devices. For example, High-Definition MultimediaInterface-Consumer Electronic Control (HDMI-CEC) simplifies the setupand operation of an otherwise complex arrangement of networked mediapresentation devices. Although the networked media devices maycommunicate via such a standardized interface, some or all of the mediapresentation devices may remain independently powered and, as such, maybe turned on and off independently.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example media monitoring system todetect an on state or an off state of a media presentation device.

FIG. 2 is a block diagram of an example on/off identifier implemented inan example back office as illustrated in FIG. 1.

FIG. 3 is a more detailed illustration of the example on/off identifierof FIGS. 1 and 2.

FIG. 4A is a detailed illustration of an example fuzzy logic engine thatmay be used to implement the example on/off identifier of FIG. 3

FIG. 4B is a representation of data flow through an example bufferduring operation of an example fuzzy contribution analyzer implementedin the example fuzzy logic engine of FIG. 4A.

FIG. 5 is a flow diagram representative of example machine readableinstructions that may be executed to implement the example on/offidentifier of FIGS. 1-3 and/or 4.

FIG. 6 is a flow diagram representative of example machine readableinstructions that may be executed to implement an example standarddeviation determiner for inclusion in the example on/off identifier ofFIG. 3.

FIG. 7 is a flow diagram representative of example machine readableinstructions that may be executed to implement an example integratedmagnitude determiner for inclusion in the example on/off identifier ofFIG. 3.

FIG. 8 is a flow diagram representative of example machine readableinstructions that may be executed to implement an example gain evaluatorfor inclusion in the example fuzzy logic engine of FIG. 4A.

FIG. 9 is a flow diagram representative of example machine readableinstructions that may be executed to implement an example remote controlhint evaluator for inclusion in the example fuzzy logic engine of FIG.4A.

FIG. 10 is a flow diagram representative of example machine readableinstructions that may be executed to implement an example magnitudestandard deviation evaluator for inclusion in the example fuzzy logicengine of FIG. 4A.

FIG. 11 is a flow diagram representative of example machine readableinstructions that may be executed to implement an example integratedmagnitude evaluator for inclusion in the example fuzzy logic engine ofFIG. 4A.

FIGS. 12A and 12B are collectively flow diagrams representative ofexample machine readable instructions that may be executed to implementan example input convergence evaluator for inclusion in the examplefuzzy logic engine of FIG. 4A.

FIG. 13 is a flow diagram representative of example machine readableinstructions that may be executed to implement an example stage onefuzzy logic evaluator for inclusion in the example fuzzy logic engine ofFIG. 4A.

FIGS. 14A and 14B are flow diagrams representative of example machinereadable instructions that may be executed to implement an example stagetwo fuzzy logic evaluator for inclusion in the example fuzzy logicengine of FIG. 4A.

FIG. 15 is a flow diagram representative of example machine readableinstructions that may be further executed in conjunction with theexample machine readable instructions of FIGS. 13 and 14 to implement anexample outlier removal method within the fuzzy logic engine of FIG. 4A.

FIG. 16 is a representation of an example output generated by an examplemicrophone gain evaluator implemented in the example on/off identifierof FIG. 3.

FIG. 17 is a representation of an example output generated by theexample standard deviation determiner of FIG. 3 executing the examplemachine accessible instructions of FIG. 9.

FIG. 18 is a representation of an example output generated by theexample integrated magnitude determiner of FIG. 3 executing the examplemachine accessible instructions of FIG. 10.

FIGS. 19A-19C are representations of example outputs generated by theexample fuzzy logic engine of FIG. 3 executing the example machineaccessible instructions of FIGS. 13-15.

FIG. 20 is a representation of an example output generated by theexample on/off identifier of FIGS. 1-2 executing the example machineaccessible instructions of FIGS. 5-15.

FIG. 21 is a block diagram of an example processor system that may beused to execute the example machine accessible instructions of FIGS.5-15 to implement the example system and/or apparatus of FIGS. 1-4A.

DETAILED DESCRIPTION

Certain examples are shown in the above-identified figures and describedin detail below. In describing these examples, like or identicalreference numbers are used to identify common or similar elements.Although the example systems and apparatus described herein include,among other components, software executed on hardware, such systems andapparatus is merely illustrative and should not be considered aslimiting. Any or all of the disclosed components could be embodiedexclusively in hardware, exclusively in software, exclusively infirmware or in some combination of hardware, firmware or software.

In the example descriptions that follow, reference is made to certainexample constant values used as, for example, thresholds, adjustmentfactors, etc. Such example constant values correspond to the exampleexperimental results illustrated in FIGS. 16-20 and discussed in greaterdetail below. However, these constant values are merely illustrativeexamples and are not meant to be limiting. For example, any or all ofthe described example constant values may be changed depending on theparticular operating environment in which the example methods and/orapparatus described herein are employed.

Metering data providing an accurate representation of the exposure tomedia content of persons in metered households is useful in generatingmedia ratings of value to advertisers and/or producers of media content.Generating accurate metering data has become difficult as the mediapresentation devices have become more complex in functionality andinteroperability. Manufacturers are developing standardized interfacesto ease the set-up and connection of these devices (e.g., such asHDMI-CEC). However, the media presentation devices may still be poweredindependently. For example, a media source device (e.g., a set top box)may be in an on state and providing media content to a mediapresentation device (e.g., a television) that is in an off state. As aresult, whereas metering data reflecting the operation of the STB ofthis example would indicate exposure to media content, in reality theexample television is “off” and, therefore, no exposure is possible.Metering data accurately representing the on states and off states ofeach media presentation device (e.g., each of the television and the settop box described above) help ensure that the media ratings accuratelyrepresent the media exposure habits of persons in metered environments.

Many existing methods for determining an on state or an off state of atelevision utilize data from sensors associated with an audiencemeasurement device located within the metered environment. For example,the sensors may detect audio signals associated with the operation oftelevisions (e.g., 15.75 kHz signals from the power unit (e.g., theflyback converter) of a CRT display), video signals (e.g. light levels),electromagnetic fields associated with a media presentation deviceand/or remote control signals (e.g., radio frequency or infraredsignals). Audience measurement devices utilizing these methods requireadditional components designed to detect the on state or the off stateof the media devices (e.g., light level detectors, electromagnetic fielddetectors, etc.), additional processor capacity to process theadditional data (e.g., detecting and filtering a 15.75 kHz signal froman audio signal) and/or additional memory to store a greater amount ofdata. Such metering devices may be large, contain multiple sensingunits, and/or be expensive to build, resulting from the need foradditional sensors, processing power and memory.

The previously known technologies to detect the on state or the offstate of a media presentation device, as discussed above, are complex toset up by a person without additional training (e.g., in locating theadditional sensors properly to obtain a signal) and/or are expensive tobuild and/or transport (e.g., because additional components add cost andweight), which may reduce the number of participants capable of beingincluded in a metering project. Further, newer television technologies(e.g., liquid crystal display (LCD) televisions, plasma televisions andprojection televisions) do not create the 15.75 kHz emissions associatedwith a flyback converter in cathode ray tube (CRT) televisions and,thus, are not conducive to on/off metering by flyback converter noisedetection.

Against this backdrop, portable audience measurement devices configuredto capture data regarding media exposure (e.g., television viewinghabits of person(s) in metered households) without the use of additionalcomponents (e.g., sensors, additional memory, etc) dedicated to sensethe on state or off state of media presentation devices are disclosedherein. More specifically, the example methods and apparatus describedherein may be used to identify the on state or the off state of mediapresentation devices (e.g., televisions, stereo receivers, etc.) fromexisting data collected by an audience measurement device over a timeperiod of interest. Portable metering devices (e.g., mailable meterswhich are the audience measurement devices designed to be sent tometering sites (e.g., households where at least one person elects toparticipate in an audience measurement panel)), installed by theparticipating person(s) at the metered site(s) and then returned to aback office for processing after a period of time, may particularlybenefit from these techniques. However, other types of meters may alsobenefit from the described techniques. In the case of a portable meter,the meter and/or the data collected by the meter are sent to a backoffice where the collected data is processed to identify the mediacontent detected in the metered household and to determine if suchdetected media content should be credited as having been presented toone or more audience members.

One method of crediting media content as being presented to one or moreaudience members is accomplished through examining signatures ofcaptured signals (e.g., a captured audio signal and/or a captured videosignal). For example, a signature may be determined from an audio signalcaptured via a microphone of a meter regardless of whether a mediapresentation device was actively presenting media content. For example,any audio signal, such as the audio content of a television program or aconversation in a room containing the meter, may be processed todetermine a signature. The signature may be used for crediting mediacontent as having been presented in a metered environment if a match isfound between the determined signature and an entry in a referencedatabase. Crediting information corresponding to such signature matchesmay be used to determine whether a media presentation device is in theon state or the off state, but signature matches alone does not provideaccurate results. For example, a television may be on and presentingmedia content without a signature match being found with the referencedatabase, such as when the media content is being provided by a digitalversatile disc (DVD). However, an unmatched signature (e.g.,corresponding to people talking in the room) may also be collected whenthe television is in the off state. Furthermore, although validcrediting information provides a strong inference that a mediapresentation device is in the on state or the off state, other factors(e.g., signature characteristics, remote control hints and/or a gain ofa microphone in a meter) utilized by the example methods and apparatusdescribed herein can improve the accuracy of the on/of determination.

To this end, the example methods and apparatus described herein obtain asignature, a gain associated with a microphone and/or hints associatedwith remote control events associated with the media presentation deviceas detected by an audience measurement device. A characteristicassociated with the signature is determined and analyzed to identify theon state or the off state of the monitored media presentation device. Inthe illustrated example, the is determined by (1) deriving a magnitudeassociated with the signature and integrating the derived magnitude overa period of time and/or (2) determining a standard deviation of amagnitude associated with the signature over a period of time.

The example methods and apparatus described herein may identify whetherthe monitored media presentation device is in the on state or the offstate based on the determined characteristic of the signature and/or again in a microphone of the audience measurement device that detectedthe media content. Alternatively or additionally, the example methodsand apparatus may identify whether the media presentation device is inthe on or the off state based on a hint from a remote control devicemonitored by the audience measurement device that detected the mediacontent or by a second audience measurement device.

In an example implementation, the gain in the microphone of the audiencemeasurement device, the hints derived from events reflecting theoperation of a remote control device and/or the characteristic(s) of thesignature magnitude are analyzed with a fuzzy logic engine within anon/off identifier. The fuzzy logic engine stores a record representingthe on state or the off state of the media presentation device over themetered period in an output database.

Referring to FIG. 1, a media content provider 102 provides content to anaudience via one or more information presentation devices, such as a settop box 104 and a television 106. The components of the mediapresentation system may be coupled in any manner. In the illustratedexample, the television 106 is positioned in a monitored area 120located within a household occupied by one or more people, representedby a person 110, some or all of whom have agreed to participate in anaudience measurement research study. The monitored area 120 includes thearea in which the television 106 is located and from which the one ormore household member(s) 110 located in the monitored area 120 may viewthe television 106.

In the illustrated example, an audience measurement system 100 is usedto collect audience measurement data concerning media activityassociated with the metered household. To this end, an audiencemeasurement device 108 is configured to collect media exposureinformation associated with one or more a media device(s) (e.g., the settop box 104 and the television 106) in the monitored area 120. Theexposure information may be collected via wired connection(s) to themedia device(s) and/or without such wired connection(s) (e.g., bymonitoring audio and/or other detectible events in the viewing area).The audience measurement device 108 provides this exposure information,which may include detected codes associated with audio content, detectedaudio signals, collected signatures representative of detected audiosignals, tuning and/or demographic information, etc. for evaluation in aback office 114. The information collected by the audience measurementdevice 108 may be conveyed to the back office 114 for evaluation byphysically sending the audience measurement device 108 to the backoffice 114 for evaluation (e.g., transporting via a courier or theUnited States Postal Service) or, alternatively, via any othernetworking connection (e.g., an Ethernet connection, the Internet, atelephone line, etc.). The information collected in the audiencemeasurement device 108 is processed and stored in the back office 114 toproduce ratings information. In the illustrated example, the back office114 includes an on/off identifier 116 to determine whether the mediapresentation device (e.g., the television 106) is in the on state or theoff state and, thus, to determine whether media detected by the audiencemeasurement device 108 should be counted as an audience exposure.

The media content provider 102 may convey the media content to a meteredhousehold via a cable network, a radio transmitter or one or moresatellites. For example, the media content provider may be a cabletelevision provider distributing the television programs exclusively viaa cable network or a satellite provider distributing media viasatellite. The media content provider 102 may transmit media signals inany suitable format, such as a National Television Standards Committee(NTSC) television signal format, a high definition television (HDTV)signal format, an Association of Radio Industries and Businesses (ARIB)television signal format, etc.

One or more user-operated remote control devices 112 (e.g., an infraredremote control device, a radio frequency remote control device, etc.)allow a viewer (e.g., the household member 110) to send commands to thetelevision 106 and/or STB 104 requesting presentation of specific mediacontent or broadcast channels provided by the media content provider102. The remote control device(s) 112 may be designed to communicatewith only a subset of the media devices (e.g., the television 106 and/orthe set top box 104) from a single manufacturer, or the remote controldevice(s) 112 may be a universal remote control configured tocommunicate with some or all of the media devices in the meteredhousehold. For example, a universal remote control device 112 may allowan audience member 110 to cause both the television 106 and the set topbox 104 to enter an on state and to configure themselves such that thetelevision 106 displays media content supplied via the set top box 104.

In the illustrated example, the audience measurement device 108 isconfigured to collect information regarding the viewing behaviors ofhousehold members 110 by monitoring a non-acoustic signal (e.g., a videosignal, an audio signal, an infrared remote control signal, etc.) and/oran acoustic signal (e.g., sound) within the monitored area 120. Forexample, the information collected may comprise an audio signalreflecting humanly audible and/or humanly inaudible sounds within thehousehold recorded via a microphone coupled to or included in theaudience measurement device 108. Additionally or alternatively, thecollected information may include signals (e.g., infrared, radiofrequency, etc.) generated by a remote control device 112. The audiorecorded via the microphone of the audience measurement device 108 maycomprise audio signals from the monitored media presentation device(e.g., the television 106) and/or background noise from within themonitored area 120. The remote control signals captured from the remotecontrol device 112 may contain control information (e.g., channel tuningcommands, power on/off commands, etc.) to control the monitored mediadevice(s) (e.g., the set top box 104 and/or the television 106).

Periodically or a-periodically, the captured audience measurement devicedata is conveyed (e.g., the audience measurement device 108 isphysically sent to the back office, the data collected is transmittedelectronically via an Ethernet connection, etc.) to the back office 114for processing. The back office 114 of the illustrated example extractsa signature from the audio captured via the microphone of the audiencemeasurement device 108. One or more characteristics of the signaturesare then analyzed alone or in conjunction with other data as explainedbelow to produce crediting information regarding programs presented by amonitored media presentation device (e.g., a radio, a stereo, a STB 104,a television 106, a game console, etc.).

In the example media monitoring system, the on/off identifier 116 isimplemented in the back office 114 and is configured to identify whethera media presentation device (e.g., the STB 104 and/or the television106) is in an on state capable of actively presenting media content, orin an off state. The information regarding the on state or off state ofthe television is helpful in accurately processing the data captured bythe audience measurement device 108. For example, the set top box 104may be in an on state such that the set top box 104 continues to receiveand output media content provided by the media content provider 102,while the television 106 may have been placed in an off state. Withoutthe information provided by the on/off identifier 116, meaning the onstate or the off state of the television 106, the media ratingsgenerated in the back office 114 from the information gathered by theaudience measurement device 108 might erroneously credit the mediacontent as having been presented to the person 110 in the meteredhousehold, when in fact, the media was not presented and no mediaexposure occurred. Thus, the on/off identifier 116 may be used toimprove the accuracy of media exposure measurements and ratings derivedtherefrom by determining whether the media content was actuallypresented to the person 110 within the monitored area 120.

FIG. 2 is a block diagram of an example system 200 implemented withinthe back office 114 for processing data when the example audiencemeasurement device 108 and/or the data collected thereby is returnedfrom a monitored area 120. The example system 200 allows the exampleon/off identifier 116 to access data gathered by the audiencemeasurement device 108 to determine whether the media presentationdevice 104, 106 was in the on state or the off state at the time thedata was gathered. As described above, the audience measurement device108 collects data (e.g., ambient audio, audio signals, video signals,remote control signals, etc.) in the metered monitored area 120.Subsequently the data is conveyed to the back office 114 to be utilizedto generate media ratings information.

The audience measurement device 108 of the illustrated example storesthe captured data within a data file 202 and then transfers the captureddata file 202 to an input database 204 implemented in the back office114. The data may, for example, be conveyed to the back office 114 viaelectronic means (e.g., transferring via an Ethernet connection) orphysical means (e.g., transporting the audience measurement device tothe back office 114). The data stored within the input database 204 isprocessed to create, for example, an audio signature for use inidentifying media presented to the meter 108 and/or other information(e.g., tuning information, program identification codes, etc.) used toidentify the media. Alternatively, audio signatures may be determined bythe audience measurement device 108 and included in the data file 202.Any mechanism for identifying media content based on the data collectedby the audience measurement device 108 can be employed without departingthe scope of this disclosure. Therefore, media content identificationmechanisms (e.g., program identification metering, signature metering,etc.) will not be further described herein. In the illustrated example,the on/off identifier 116 obtains data (e.g., the audio signal, thesignature, a characteristic of the signature, the remote control eventrecord(s), etc.) from the input database 204 to determine whether themedia presentation device (e.g., the television 106) is in the on stateor the off state.

The data captured by the audience measurement device 108 may be storedin the data file 202 in any format (e.g., an American Standard Code forInformation Interchange (ASCII) format, a binary format, a raw dataformat, etc.) for storing data on an electronic medium (e.g., a memoryor a mass storage device). The electronic medium may be a non-volatilememory (e.g., flash memory), a mass storage device (e.g., a disk drive),a volatile memory (e.g., static or dynamic random access memory) and/orany combination of the memory types. For example, the data file 202 maybe stored in binary format on a random access memory 2108communicatively coupled to a processor 2102 within a processor system2100, such as the processor system 2100 described in detail below inconjunction with FIG. 21.

In some example implementations, the data captured by the audiencemeasurement device 108 may undergo some or all of the on/off detectionprocessing (e.g., determining an audio signature) within the audiencemeasurement device 108 itself, with the results being stored within thedata file 202 within the audience measurement device 108.

A block diagram of an example implementation of the on/off identifier116 of FIGS. 1 and 2 is depicted in FIG. 3. The example on/offidentifier 116 includes a data collector 306, a signature characteristicdeterminer 310, a fuzzy logic engine 316 and an output database 318. Theexample data collector 306 collects data (e.g., audio gain data, remotecontrol hints, audio signatures, etc.) from the example input database204 containing data obtained from, for example, the metered household120 with the audience measurement device 108. The signaturecharacteristic determiner 310 determines a characteristic of a signatureobtained or determined from data in the input database 204. For example,while the signature may be created during analysis in the back office114, the signature generation functionality may alternatively beintegrated into the audience measurement device 108 and the resultingdetermined signature transferred to the example input database 204(e.g., in the data file 202).

The example fuzzy logic engine 316 of FIG. 3 identifies whether themonitored media presentation device 104,106 is in the on state or theoff state. An example implementation of the fuzzy logic engine 316 isdescribed in detail below in conjunction with FIG. 4A. The on/off statesidentified by the fuzzy logic engine 316 are stored in the outputdatabase 318 and made available for further analysis (e.g., of the datacollected with the audience measurement device 108).

While the input database 204 (FIG. 2) and the output database 318 aredepicted as separate blocks within the back office 116, their respectivefunctionality may be incorporated within a single database orimplemented with two or more databases. Furthermore, the input database204 of FIG. 2 and the output database 318 of FIG. 3 may be implementedas any type of database (e.g., a delimited flat file database or astructured query language (SQL) relational database) and storedutilizing any data storage method (e.g., a flash memory, a mass storagedevice, static or dynamic random access memory, etc.).

The example data collector 306 of FIG. 3 includes a remote control hintcollector 302, a microphone gain collector 304 and a signature collector308. The remote control hint collector 302 collects hints associatedwith the operation of a remote control device (e.g., the remote control112) within a metered viewing area (e.g., the metered monitored area120) from the data file 202. The hints may comprise any communicationbetween the remote control device 112 and a monitored media device(e.g., the television 106 or the set top box 104) collected by anaudience measurement device (e.g., the audience measurement device 108).For example, the remote control 112 may transmit commands entered by aperson 110 to a television 106 via infrared signals. The audiencemeasurement device 108 of the illustrated example is configured tocapture the infrared commands and store the captured commands in thedata file 202 (FIG. 2) along with a time stamp indicating when the datawas captured and stored. The remote control hint collector 302 of FIG. 3collects hints from the stored data to be analyzed by the fuzzy logicengine 316.

The microphone gain collector 304 of the illustrated example collectsthe gain information associated with a microphone of the audiencemeasurement device 108 from the input database 204 for analysis by thefuzzy logic engine 316. As noted above, the microphone captures ambientaudio present in the monitored area 120. This audio includes any audiooutput of the monitored media presentation device (e.g., the television106, a stereo (not shown), etc.) and other background noise (e.g., noisegenerated inside or outside the monitored area 120, conversations amongthe household members, etc.). The gain applied to the microphone isinversely proportional to the amplitude of the audio captured by themicrophone. A high level of gain corresponds with a low level of ambientaudio captured by the microphone. Conversely, a low level of gaincorresponds with a high level of audio captured by the microphone.

As described above, the audio signal output by the microphone may beanalyzed either in the audience measurement device 108 or in the backoffice 114 to determine an audio signature associated with media contentpresented by, for example, the television 106. The signature is thencompared to reference signatures related to known programming providedby the media content provider 102. When a signature associated with themonitored audio signal is found to match with a reference signature, theprogram associated with the reference signature is identified as themedia content presented by the television 108 and used in generating themedia ratings data.

The signature collector 308 of FIG. 3 collects the audio signature fromthe input database 204 or from a signature generator (not shown)configured to process audio data stored in the input data base 204. Thesignature characteristic determiner 310 of the illustrated exampledetermines a characteristic associated with the signature for analysisby the fuzzy logic engine 316. In particular, the example signaturecharacteristic determiner 310 determines and/or derives the magnitudeassociated with the signature. The magnitude of a signature will varyover time, depending on the type of signature employed. In theillustrated example, the signature reflects, for example, time domainvariations of the audio signal captured by the audience measurementdevice 108. Accordingly, the magnitude of the signature reflectsvariations of the audio amplitude. To reduce the time varying magnitudefor a given time period to a single value, the signature characteristicdeterminer 310 includes an integrated magnitude determiner 312. Theintegrated magnitude determiner 312 integrates the magnitude of thesignature over the period of time. The integrated magnitude may serve asthe characteristic of the system utilized by the fuzzy logic engine 315as described below. Alternatively or additionally, the signaturemagnitude may be analyzed by the magnitude standard deviation determiner314 to determine a standard deviation of the magnitude associated withthe signature over the period of time. In example apparatus employing amagnitude standard deviation determiner 314, the standard deviation mayserve as the characteristic used by the fuzzy logic engine 316.

The fuzzy logic engine 316 analyzes the data (e.g., the remote controlhints, the microphone gain, the integrated magnitude of the signatureand/or the standard deviation of the magnitude of the signature)collected by the data collector 306 and/or determined by the signaturecharacteristic determiner 310 to identify whether the monitored mediapresentation device 104,106 is in the on state or the off state. Oncethe on state or off state is determined by the fuzzy logic engine 316,the states are stored in the output database 318. The states are storedin association with timestamps reflecting the time at which thecorresponding signature occurred. The example on/off identifier 118utilizes a fuzzy logic engine 316 to determine the on state or the offstate, but any other analysis method may be used.

While an example manner of implementing the on/off identifier 116 ofFIGS. 1-2 has been illustrated in FIG. 3, one or more of the elements,blocks and/or devices illustrated in FIG. 3 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example data collector 306, the example remote control hintcollector 302, the example microphone gain collector 304, the examplesignature collector 308, the example signature characteristic determiner310, the example integrated magnitude determiner 312, the examplemagnitude standard deviation determiner 314, the example fuzzy logicengine 316, and/or the example output database 318 and/or, moregenerally, the on/off identifier 116 of FIGS. 1-3 may be implemented byhardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the example datacollector 306, the example remote control hint collector 302, theexample microphone gain collector 304, the example signature collector308, the example signature characteristic determiner 310, the exampleintegrated magnitude determiner 312, the example magnitude standarddeviation determiner 314, the example fuzzy logic engine 316, theexample output database 318 and/or, more generally, the example on/offidentifier 116 could be implemented by one or more circuit(s),programmable processor(s), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)), etc. When any of the appendedclaims are read to cover a purely software and/or firmwareimplementation, at least one of the example data collector 306, theexample remote control hint collector 302, the example microphone gaincollector 304, the example signature collector 308, the examplesignature characteristic determiner 310, the example integratedmagnitude determiner 312, the example magnitude standard deviationdeterminer 314 the example fuzzy logic engine 316, and/or the exampleoutput database 318 are hereby expressly defined to include a tangiblemedium such as a memory, DVD, CD, etc. storing the software and/orfirmware. Further still, the on/off identifier of FIGS. 1-3 may includeone or more elements, processes and/or devices in addition to, orinstead of, those illustrated in FIG. 3, and/or may include more thanone of any or all of the illustrated elements, processes and devices.

A block diagram depicting an example implementation of the example fuzzylogic engine 316 of FIG. 3 is illustrated in FIG. 4A. The exampleimplementation of the fuzzy logic engine 316 comprises a gain evaluator402, a remote control hint evaluator 404, a standard deviation evaluator406, an integrated magnitude evaluator 408, an input convergenceevaluator 410, a fuzzy contribution analyzer 412 and a creditingcontribution analyzer 414. The example fuzzy logic engine 316 may beimplemented using any desired combination of hardware, firmware and/orsoftware. For example, one or more integrated circuits, processingdevices, discrete semiconductor components and/or passive electroniccomponents may be used to implement the example fuzzy logic engine 316.

Generally, the fuzzy logic engine 316 is designed to analyze datacollected via the audience measurement device 108 to determine whether amonitored media presentation device 104, 106 was in an on state or anoff state during time intervals within a monitored period. Morespecifically, the example audience measurement device 108 captures data(e.g., ambient audio, an audio signal, a remote control event record,etc.) at specific intervals (e.g., at 0.5 second increments) within thesampling period (e.g., one month) and stores the data in the data file202 along with a timestamp corresponding with the time and date the datawas captured. When transferred to the input database 204, the timestampsremain associated with the corresponding captured data and, preferably,with the data derived therefrom. The fuzzy logic engine 316 operates atan engine cycle corresponding to a time interval of, for example, 2seconds, and separately evaluates the data captured for each enginecycle.

For each engine cycle, each of the gain evaluator 402, the remotecontrol hint evaluator 404, the standard deviation evaluator 406, andthe integrated magnitude evaluator 408 evaluates the corresponding datacollected by the data collector 306 and/or the signaturecharacteristic(s) determined by the signature characteristic determiner310 to generate a fuzzy contribution value. In the illustrated example,the gain evaluator 402 generates a first fuzzy contribution value, theremote control hint evaluator 404 generates a second fuzzy contributionvalue, the standard deviation evaluator 406 generates a third fuzzycontribution value and the input convergence evaluator 408 generates afourth fuzzy contribution value.

Additionally, the input convergence evaluator 410 further evaluates eachof the generated fuzzy contribution values (e.g., the first fuzzycontribution value, the second fuzzy contribution value, the third fuzzycontribution value and the fourth fuzzy contribution value) to determinewhether the first, second, third and fourth fuzzy contribution valuesconverge toward an indication of an on state (e.g., a positive value).The input convergence evaluator 410 increments an audio test score valueby the number of fuzzy contribution values that converge toward an onstate. If the input convergence evaluator 410 determines that theevaluated first, second, third and fourth fuzzy contribution valueconverges towards an indication of an off state (e.g., a negativevalue), the audio test score is not incremented. After adjusting theaudio test score, the input convergence evaluator 410 also analyzes theaudio test score value to determine a fifth fuzzy contribution valueassociated with the number of evaluators that converge to (e.g.,indicate) an on state. A new audio test score is calculated for eachengine cycle. The audio test score and the first through fifth fuzzycontribution values are specific to each engine cycle.

After a period of time encompassing several engine cycles (e.g., twentyfour hours), the first, second, third, fourth and fifth fuzzycontribution values generated by the gain evaluator 402, the remotecontrol hint evaluator 404, the standard deviation evaluator 406, theintegrated magnitude evaluator 408, and the input convergence evaluator410, respectively, are further analyzed to generate a recordcorresponding to the operating state(s) (e.g., the on state or the offstate) of the monitored media presentation device during the exampletwenty four hour period.

The example gain evaluator 402 to evaluates a gain signal collected bythe microphone gain collector 304 from the input database 204 (FIG. 2).The gain evaluator 402 outputs the first fuzzy contribution value to beanalyzed by the fuzzy contribution analyzer 412 and by the inputconvergence evaluator 410. The gain signal evaluated by the gainevaluator 402 of the illustrated example may comprise a range of valuescorresponding to a decibel (dB) range captured by a microphone over aperiod of time. For example, a mailable meter provided by The NielsenCompany, Inc., includes a microphone and is capable of applying a gainin the range of 0 dB to a maximum of 59.5 dB to the microphone in stepincrements of 0.5 dB per step.

In the evaluation process, the gain evaluator 402 examines the gainvalue for the engine cycle and generates a first fuzzy contributionvalue associated with the same engine cycle. The first fuzzycontribution value is proportional to the gain input value in decibels.The gain evaluator 402 generates a positive first fuzzy contributionvalue for small gain values, because small gain values imply a highvolume audio signal. Conversely, a large gain value implies a low volumeaudio signal and, thus, the gain evaluator 402 generates a negativefirst fuzzy contribution value proportional to the gain input value indecibels. Additionally, a microphone may capture a high volume levelwhen a person or persons are speaking within a metered viewing area(e.g., the monitored area 120) or when a media device (e.g., thetelevision 106) is producing a high volume audio output. Consequently,the positive contribution of the gain value is limited to a maximumfirst fuzzy contribution value. A negative first fuzzy contributionvalue, corresponding to low volume levels, is not limited to a minimumvalue

The remote control hint evaluator 404 of the illustrated exampleevaluates a series of remote control hints collected by the remotecontrol hint collector 302. The remote control hints correspond with,for example, commands issued by the participating viewer 110 to amonitored media device 104 and/or 106 via the remote control device 112.Hints contribute to the second fuzzy contribution value when a hintimplies that the household member 110 was exposed to media contentpresented via the monitored media presentation device 104, 106. Forexample, a hint implies that the household member was exposed to mediacontent presented via the media presentation device 104, 106 when thehint occurs (1) within fifteen minutes of a second hint and (2) thesecond hint occurs within (plus or minus) fifteen minutes of the currentevaluated time (e.g., the time associated with the current enginecycle). This rule assumes that an active audience member will use theremote control to adjust the monitored media presentation device(s) 104and/or 106 at least twice every 30 minutes.

The standard deviation evaluator 406 of the illustrated exampleevaluates a standard deviation of a magnitude of a signature, asdetermined by, for example, the magnitude standard deviation determiner314 over a time period (e.g., 15 seconds). The standard deviation of themagnitude of a signature may be highly variable, so the values outputfrom the magnitude standard deviation determiner 314 represent lowerbound standard deviation (LBSD) values calculated (e.g., filtered) overa period of time. In some example implementations of the standarddeviation determiner 314, the standard deviation value of the currentengine cycle is inserted into a lower bound filter. The example filtermay be implemented via a circular buffer (e.g., a first-in-first-outbuffer with 120 elements) that outputs the minimum value containedwithin the buffer as the LBSD. The filtered output from the magnitudestandard deviation determiner 314 (i.e., the LBSD) is then evaluated inthe standard deviation evaluator 406. The standard deviation evaluator406 determines the third fuzzy contribution value via an equation thatmay be determined through an examination of experimental results. Forexample, experimental results have indicated that an off statecorresponds to very low standard deviation values (e.g., under 10) andan on state correlates to standard deviation values within anintermediate range (e.g., between 10 and 20). From these results, anexample equation may be inferred where an LBSD value greater than athreshold within the indication range of an on state, (e.g., +15)generate a positive third fuzzy contribution value, and an LBSD valueless that the threshold generates a negative third fuzzy contributionvalue. Additionally, the experimental results demonstrated that an offstate also corresponded to very high standard deviation values (e.g.,greater than 35), so another example equation may incorporate thisexperimental result as an additional way to determine the third fuzzycontribution value.

The integrated magnitude evaluator 408 of the illustrated exampleevaluates the signal output by the integrated magnitude determiner 312.The output signal of the integrated magnitude determiner 312 representsan integrated magnitude of a signature over a period of time. Theintegrated magnitude evaluator 408 generates the fourth fuzzycontribution value by evaluating an first equation corresponding to theintegrated magnitude value, for example, subtracting a first constant(e.g., 55) from the integrated magnitude value The first constantrepresents a threshold value of the integrated magnitude representingthe lowest end of a range of experimentally determined values thatindicate an on state of a media presentation device. For example,experimental results from an example implementation depicted in FIG. 18demonstrate that an on state corresponds with integrated magnitudevalues in a range between +55 and +95 and an off state corresponds withintegrated magnitude values in the range between −21 and +22). Thefourth fuzzy contribution value is set equal to the value of theintegrated magnitude less the first constant if that difference ispositive. A negative fourth fuzzy contribution value is also possible.In particular, if the difference between the integrated magnitude andthe first constant is negative, the difference may be multiplied by asecond constant value (e.g., 2) and/or evaluated with a second equationto cause the negative fourth fuzzy contribution of the integratedmagnitude evaluator 408 to have a greater influence in the analysisperformed by the fuzzy contribution analyzer 412. A negative fourthfuzzy contribution value may be due to, for example, a change in gain ofthe audio signal used to create the signature or a change in, oroccurring during, a normalization process for the signature.

Each of the first fuzzy contribution value, the second fuzzycontribution value, the third fuzzy contribution value and the fourthfuzzy contribution value is evaluated in the input convergence evaluator410 to generate a fifth fuzzy contribution. The fifth fuzzy contributionvalue indicates the number of evaluators that generated a positive fuzzycontribution value (e.g., converged to the on state indication) for theevaluated engine cycle. More specifically, at the start of each enginecycle an audio test score counter 416 within the input convergenceengine 410 is initialized (e.g., set to a null value). Next, the exampleinput convergence evaluator 410 examines the first fuzzy contributionvalue output from the gain evaluator 402. If the first fuzzycontribution value is positive (e.g., a value greater than 0), then thefirst fuzzy contribution value converges towards the on state indicationand the audio test score counter 416 is incremented. Conversely, if thefirst fuzzy contribution value is a value of zero or less (e.g., anegative value), the audio test score counter 416 is not incremented dueto the evaluation of the first fuzzy contribution value.

The example input convergence evaluator 410 then examines the secondfuzzy contribution value output from the remote control hint evaluator404. If the second fuzzy contribution value is positive (e.g., a valuegreater than 0), then the second fuzzy contribution value convergestowards the on state indication and the audio test score counter 416 isincremented. Conversely, if the second fuzzy contribution value is avalue of zero or less (e.g., a negative value), the audio test scorecounter 416 is not incremented due to the evaluation of the second fuzzycontribution value.

The example input convergence evaluator 410 then examines the thirdfuzzy contribution value output from the standard deviation evaluator406. If the third fuzzy contribution value is positive (e.g., a valuegreater than 0), then the third fuzzy contribution value convergestowards the on state indication and the audio test counter 416 isincremented. Conversely, if the third fuzzy contribution value is avalue of zero or less (e.g., a negative value), the audio test scorecounter is not incremented as a result of the evaluation of the thirdfuzzy contribution value.

The example input convergence evaluator 410 then examines the fourthfuzzy contribution value output from the integrated magnitude evaluator408. If the fourth fuzzy contribution value is positive (e.g., a valuegreater than 0), then the fourth fuzzy contribution value convergestowards the on state indication and the audio test score counter 416 isincremented. Conversely, if the fourth fuzzy contribution value is avalue of zero or less (e.g., a negative value), the audio test scorecounter 416 is not incremented as a result of the evaluation of thefourth fuzzy contribution value.

The value in the audio score counter 416 is then analyzed by the inputconvergence evaluator 410 to identify the number of evaluators thatgenerated a positive fuzzy contribution value for the evaluated enginecycle. In particular, the input convergence evaluator 410 generates afifth fuzzy contribution value that is proportional to the number ofevaluators that incremented the audio test score value (e.g., the numberof evaluators that had positive fuzzy contribution values). The inputconvergence evaluator 410 generates the fifth fuzzy contribution valueby assigning a negative value to the fifth fuzzy contribution value whentwo or less evaluators incremented the audio test score counter 416(i.e., the counter 416 has a value of 2 or less) or a positive value tothe fifth fuzzy contribution value when three or more evaluatorsincremented the audio test score counter 416 (i.e., the counter 416 hasa value of 3 or more). In the illustrated example, if the value in theaudio test score counter 416 is zero, then the fifth fuzzy contributionvalue is assigned a value of −40, if the value in the audio test scorecounter 416 is 1, the fifth fuzzy contribution value is assigned a valueof −30, if the value in the audio test score counter 416 is three, thenthe fifth fuzzy contribution value is assigned a value of +10, and ifthe value in the audio test score counter 416 is four, then the fifthfuzzy contribution value is assigned a value of +30.

The fuzzy contribution analyzer 412 of the example fuzzy logic engine316 analyzes the first, second, third, fourth and fifth fuzzycontribution values produced by the aforementioned evaluators 402-410.For each engine cycle, the fuzzy contribution analyzer 412 sums orotherwise combines the first, second, third, fourth and fifth fuzzycontribution values from the gain evaluator 402, the remote control hintevaluator 404, the standard deviation evaluator 406, the integratedmagnitude evaluator 408 and the input convergence evaluator 410,respectively, and stores the combined value as an intermediate fuzzyscore. The intermediate fuzzy score may be positive or negative andrepresents a sum of the first, second, third, fourth and fifth fuzzycontributions for the engine cycle. The intermediate fuzzy score isstored, for example, in a buffer or in any other manner with theintermediate fuzzy score values of previous engine cycles. Subsequently,the fuzzy contribution analyzer 412 processes the stored intermediatefuzzy score values for a specified first time period (e.g., 15 seconds)to discard outliers, (e.g., with any outlier determination algorithm).Following the removal of the outliers, the remaining intermediate fuzzyscore values are averaged to determine a final fuzzy score value thatcorrelates with either an on state (e.g., a positive value) or an offstate (e.g., a negative value) of the evaluated engine cycle.

FIG. 4B depicts data flow through the circular buffer 456 duringoperation of the example fuzzy contribution analyzer 412 to determinethe final fuzzy score value. In the example of FIG. 4B, two instances intime are shown. A first time instance is reflected in the leftmostimage/column. A second instance that occurs ten engine cycles after thefirst instance is shown in the rightmost image/column. As previouslymentioned the fuzzy contribution analyzer 412 sums and/or combines thefirst, second, third, fourth, and fifth fuzzy contribution values eachengine cycle into an intermediate fuzzy score. The intermediate fuzzyscore produced by each engine (e.g., intermediate fuzzy score 30 forengine cycle 30) is inserted into the circular buffer 456. In theexample of FIG. 4B, the circular buffer 456 stores 30 elements. Thus,the intermediate fuzz scores for engine cycles 1-30 are shown in thebuffer 456. When the buffer 456 is full, the most recent intermediatefuzzy score overwrites or otherwise replaces the oldest value within thebuffer 456. In the example of FIG. 4B, if there was an engine cycle 0,the intermediate fuzzy score 30 would have replaced the intermediatefuzzy score from engine cycle 0 (i.e., the engine cycle that occurred 30cycles ago).

As mentioned above, the example circular buffer 456 contains thirtyelements. Each of the elements contains an intermediate fuzzy scoredetermined during an individual engine cycle (e.g., a first engine cyclecorresponds with a first intermediate fuzzy score, a second engine cyclecorresponds with a second intermediate fuzzy score, etc.). Since in theillustrated example, each engine cycle has an associated time of twoseconds, the circular buffer 456 with thirty elements corresponds tosixty seconds of intermediate fuzzy scores.

The fuzzy contribution analyzer 412 of the illustrated exampleperiodically (e.g., once every ten seconds) processes the intermediatefuzzy scores in the circular buffer 456 to remove outliers 458. Theoutliers may be removed, for example, by using the example machinereadable instructions discussed in conjunction with FIG. 15 below. Forexample, three outliers, namely, the intermediate fuzzy score 1, theintermediate fuzzy score 6 and the intermediate fuzzy score 28, arediscarded from the buffer 456 upon completion of engine cycle 30. Oncethe outliers 458 are discarded at the end of engine cycle 30, theremaining intermediate fuzzy scores in the circular buffer 456 areaveraged to determine the final fuzzy score 1.

The above process continues with the circular buffer 454 being filledand/or overwritten each engine cycle, and the outliers being discardedand the final fuzzy score being calculated every ten seconds. In theexample of FIG. 4A, after completion of engine cycle 40, outlier 35 iseliminated and the final fuzzy score 2 is determined.

Returning to FIG. 4A, the final fuzzy score values described above arefurther processed by the fuzzy contribution analyzer 412 in anormalization and filtering process. Generally, the normalization andfiltering process performed by the fuzzy contribution analyzer 412 (1)examines the final fuzzy score values determined for a given time (e.g.,twenty-four hour) period, (2) determines the minimum final fuzzy scorevalue and maximum final fuzzy score value for the time period, and (3)generates a correction amount value proportional to the differencebetween the minimum and maximum values that may be applied to each finalfuzzy score for the above-mentioned time period. The normalized finalfuzzy scores may then be analyzed with a smoothing filter, an extremaengine, etc. An example extrema engine determines the largest absolutefinal fuzzy score value for the time period (e.g., a time periodassociated with thirty normalized final fuzzy score values) and assignsthe determined largest absolute final fuzzy score value to each of thethirty final fuzzy score values within the analyzed time period.

Once the fuzzy contribution analyzer 412 determines the normalized andfiltered final fuzzy score values, the crediting contribution analyzer414 employs the program identification data generated based on theinformation collected via the audience measurement device 108 (FIG. 1)to adjust the final fuzzy contribution values. In particular, if a givenfinal fuzzy score is associated with a time period during which themedia content is positively identified (e.g., the collected signaturematches a reference in the signature reference database), the creditingcontribution analyzer 414 increases the final fuzzy score by apredetermined amount (e.g., by adding a constant such as 150 to thefinal fuzzy score). If, on the other hand, the given final fuzzy scoreis associated with a time period during which the media content is notpositively identified (e.g., the collected signature does not match areference in the signature reference database), the creditingcontribution analyzer 414 decreases the final fuzzy score by apredetermined amount (e.g., by subtracting a constant such as 150 fromthe final fuzzy score).

After the crediting contribution analyzer 414 has adjusted the finalfuzzy scores based on the crediting result, the example creditor 418examines the final fuzzy score values over a time period (e.g., 10 or 15seconds) to determine whether or not the monitored informationpresentation device was in an on state or an off state and, thus,whether a program associated with the time period should be credited asan actual exposure to media content. The creditor 418 determines a starttime (e.g., a time associated with the metered data) and gathers mediaexposure data, from the data file 202. The creditor 418 retrieves atimestamp associated with the gathered media exposure data to determinethe final fuzzy value corresponding to the timestamp. Next, the creditor418 analyzes the final fuzzy value to determine whether the mediapresentation device was in an on state or an off state. If the mediapresentation device was off, then the creditor 418 marks the mediaexposure data as not being exposed to a viewer to ensure that the datais not credited as a media exposure of the household member 110 prior toloading the next media exposure data to be analyzed.

While an example manner of implementing the fuzzy logic engine 316 ofFIG. 3 has been illustrated in FIG. 4A, one or more of the elements,processes and/or devices illustrated in FIG. 4A may be combined,divided, re-arranged, omitted, eliminated and/or implemented in anyother way. Further, the example gain evaluator 402, the example remotecontrol hint evaluator 404, the example standard deviation evaluator406, the example integrated magnitude evaluator 408, the example inputconvergence evaluator 410, the example fuzzy logic contribution analyzer412, the example crediting contribution analyzer 414 and/or the creditor418 and/or, more generally, the example fuzzy logic engine 316 of FIG.4A may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example gain evaluator 402, the example remote control hintevaluator 404, the example standard deviation evaluator 406, the exampleintegrated magnitude evaluator 408, the example input convergenceevaluator 410, the example fuzzy logic contribution analyzer 412 and/orthe example crediting contribution analyzer 414 and/or, more generally,the example fuzzy logic engine 316 could be implemented by one or morecircuit(s), programmable processor(s), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)), etc. When any of the appendedclaims are read to cover a purely software and/or firmwareimplementation, at least one of the example gain evaluator 402, exampleremote control hint evaluator 404, example standard deviation evaluator406, example integrated magnitude evaluator 408, example inputconvergence evaluator 410, example fuzzy logic contribution analyzer 412and/or the example crediting contribution analyzer 414 are herebyexpressly defined to include a tangible medium such as a memory, DVD,CD, etc. storing the software and/or firmware. Further still, theexample fuzzy logic engine 316 of FIG. 4A may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 4A, and/or may include more than one of any or allof the illustrated elements, processes and devices.

Flowcharts representative of example machine readable instructions thatmay be executed to implement the on/off identifier 116 of FIGS. 1-4A areshown in FIGS. 5 through 15. In these examples, the machine readableinstructions represented by each flowchart may comprise one or moreprograms for execution by: (a) a processor, such as the processor 2102shown in the example processor system 2100 discussed below in connectionwith FIG. 21, (b) a controller, and/or (c) any other suitable device.The one or more programs may be embodied in software stored on atangible medium such as, for example, a flash memory, a CD-ROM, a floppydisk, a hard drive, a DVD, or a memory associated with the processor2102, but the entire program or programs and/or portions thereof couldalternatively be executed by a device other than the processor 2102and/or embodied in firmware or dedicated hardware (e.g., implemented byan application specific integrated circuit (ASIC), a programmable logicdevice (PLD), a field programmable logic device (FPLD), discrete logic,etc.). In addition, some or all of the machine readable instructionsrepresented by the flowchart of FIGS. 5 through 15 may be implementedmanually. Further, although the example machine readable instructionsare described with reference to the flowcharts illustrated in FIGS. 5through 15, many other techniques for implementing the example methodsand apparatus described herein may alternatively be used. For example,with reference to the flowcharts illustrated in FIGS. 5 through 15, theorder of execution of the blocks may be changed, and/or some of theblocks described may be changed, eliminated, combined and/or subdividedinto multiple blocks.

Example machine readable instructions 500 that may be executed toimplement the on/off identifier 116 of FIGS. 1-4A, including the exampledata collector 306, the example remote control hint collector 302, theexample microphone gain collector 304, the example signature collector308, the example signature characteristic determiner 310, the exampleintegrated magnitude determiner 312, the example magnitude standarddeviation determiner 314, the example fuzzy logic engine 316, and/or theexample output database 318, the example gain evaluator 402, the exampleremote control hint evaluator 404, the example standard deviationevaluator 406, the example integrated magnitude evaluator 408, theexample input convergence evaluator 410, the example fuzzy logiccontribution analyzer 412, the example crediting contribution analyzer414 and/or the creditor 418 are represented by the flowchart shown inFIG. 5. The example machine readable instructions 500 are executed todetermine whether a media presentation device (e.g., the STB 104 and/orthe television 106) located within a monitored viewing area (e.g., themonitored area 120) and monitored via an audience measurement device(e.g., the audience measurement device 108) is in an on state or an offstate. While the example machine readable instructions 500 are shown tobe executed within a back office (e.g., the back office 114 of FIG. 1),the instructions may be executed anywhere that the data collected viathe audience measurement device 108 may be accessed. For example, theexample machine readable instructions 500 may be executed within theaudience measurement device 108. Furthermore, the example machinereadable instructions 500 may be executed at periodic or aperiodicintervals, based on an occurrence of a predetermined event (e.g., a fullor nearly full memory), etc., or any combination thereof.

The example machine readable instructions 500 of FIG. 5 initially causethe data collector 306 of the on/off identifier 116 to extract and/orcollect data (e.g., a microphone gain record, a remote control hintrecord, a signature record, etc.) from the input database 204 containingaudience measurement data collected at the monitored area 120 with theaudience measurement device 108 (block 502). The microphone gaincollector 304 of the on/off identifier 116 then extracts a gain appliedto a microphone associated with the audience measurement device 108 fromthe input database 204 (block 504). The gain collected from the inputdatabase 204 may, for example, represent the actual gain applied to themicrophone while collecting audio in the metered monitored area 120 ormay be additionally processed (e.g., filtered). Next, the remote controlhint collector 302 collects remote control hint(s) corresponding to theremote control commands captured by the audience measurement device 108corresponding to the remote control 112 operated by a person (e.g., thehousehold member 110) (block 506). The remote control hint collector 302collects the remote control hints from the input database 204.

Next, the signature collector 308 of the on/off identifier 116 collectsa signature from the input database 204, determines a characteristic ofthe signature (e.g., the magnitude of the signature) and creates inputsto be analyzed (blocks 508-512). For example, the signature collector508 of the illustrated example collects a signature stored in the inputdatabase 204 and extracted from ambient audio recorded by the audiencemeasurement device 108 (block 508). Alternatively, the signature can beextracted from audio obtained from a wired connection to the STB 104and/or the television 106. The integrated magnitude determiner 312 ofthe signature characteristic determiner 310 integrates the magnitude ofthe signature over a period of time (e.g., 7.5 seconds) (block 510). Astandard deviation signature characteristic determiner 314 determines avalue representing the standard deviation of the magnitude for the sameor a different period of time (e.g., 15 seconds) (block 512).

The determined at blocks 502-512 are then analyzed via the example fuzzylogic engine 316 to generate the fuzzy logic values described above(block 514). Following the analysis of the inputs, the fuzzy logicengine normalizes (i.e. calculates a correction value) and filters(i.e., applies a filter comprising an extrema engine) to the results ofthe analysis from block 510 (block 516). Then, the example on/offidentifier 116 identifies whether a media presentation device (e.g.,such as the television 106) is in the on state or the off state duringthe corresponding periods of time metered with the audience measurementdevice 108 based on the normalized/filtered final fuzzy logic values(block 518).

Example machine readable instructions 600 that may be executed toimplement the magnitude standard deviation determiner 314 of FIG. 3and/or used to implement block 512 of FIG. 5 to determine a standarddeviation of a magnitude associated with a signature over a specifiedtime period (for brevity hereafter referred to as the standarddeviation) are represented by the flowchart shown in FIG. 6. The examplemachine readable instructions are executed to calculate the standarddeviation for each sample period (e.g., 15 seconds) in the time periodduring which it is desired to determine the on state and/or off state ofthe media presentation device. While, the example instructions 600 ofFIG. 6 are shown to be executed within an on/off identifier (e.g., theexample on/off identifier 116) the instructions may be executed anywherethat the data collected via the audience measurement device 108 may beaccessed. For example, the example machine readable instructions 600 maybe executed within the audience measurement device 108. Furthermore, theexample machine readable instructions 600 may be executed at periodic oraperiodic intervals, based on an occurrence of a predetermined event(e.g., a full or near full memory), etc., or any combination thereof.

The example machine readable instructions 600 of FIG. 6 begin when themagnitude standard deviation determiner 314 determines the magnitudevalues associated with signature(s) corresponding to a specified timeperiod (e.g., 15 seconds) (block 602). Next, the standard deviationdeterminer 314 calculates a standard deviation for the magnitude valuesdetermined in block 602 (block 604). Any appropriate method(s) ofcalculating standard deviations and associated characteristics ofstandard deviations may be used.

Next, the standard deviation determiner 314 determines the lower boundof a set of standard deviation(s) (block 606). In the illustratedexample, the standard deviation determiner 314 implements a circularbuffer to determine a sliding value of standard deviation values. Thecurrent calculated standard deviation overwrites the oldest standarddeviation in the circular buffer. The circular buffer may store, forexample, 120 elements storing standard deviation values calculated for a15-second time period (block 608). As each new standard deviation valueis added to the buffer, the magnitude standard deviation determiner 314calculates a new lower bound standard deviation value for the elementswithin the circular buffer (block 608). Although the magnitude standarddeviation determiner 314 of the illustrated example determines a lowerbound standard deviation value, any other value associated with astandard deviation (e.g., an upper bound) may alternatively bedetermined.

Example machine readable instructions 700 that may be executed toimplement the integrated magnitude determiner 312 of FIG. 3 and/or usedto implement block 510 of FIG. 5 are represented by the flowchart shownin FIG. 7. The example machine readable instructions 700 of FIG. 7 areexecuted to calculate the integrated magnitude associated withsignature(s) corresponding to each sample period (e.g., 7.5 seconds).While the example machine readable instructions 700 are shown to beexecuted within the example on/off identifier 116, the instructions maybe executed anywhere that data collected via the audience measurementdevice 108 may be accessed. For example, the example machine readableinstructions 700 may be executed within the audience measurement device108. Furthermore, the example machine readable instructions 600 may beexecuted at periodic or aperiodic intervals, based on an occurrence of apredetermined event (e.g., a full or near full memory), etc., or anycombination thereof.

The example machine readable instructions 700 of FIG. 7 begin by causingthe integrated magnitude determiner 312 to normalize the magnitudevalues of the signature(s) taken within the sample time period (e.g.,7.5 seconds) (block 702). A normalized magnitude is calculated by theintegrated magnitude determiner 312 for each magnitude value associatedwith the sample period by adding a correction value to each magnitudevalue. The correction value may be a constant or a result of an equationbased on factors associated with an example implementation. For example,a correction factor may be determined to be the gain of the microphoneused to collect the signature at the collection time divided by 5 basedon characteristics of the microphone. Next, the integrated magnitudedeterminer 312 averages the normalized magnitude data values calculatedover the sample time period (e.g., 2 seconds) (block 704). Theintegrated magnitude determiner 312 then integrates the normalizedmagnitude values over a sample period (e.g., approximately 7.5 seconds)(block 706). An example integration calculation is given by thefollowing equation: SUM (M*ΔT)/T where M is the average normalizedmagnitude, ΔT is the time between the magnitude data values summedwithin the time period and T is the time period of the sample. Theintegrated magnitude determiner inserts the result of the integrationcalculation into a filter (e.g., a lower bound filter) (block 708). Inthe illustrated example, a lower bound filter within the integratedmagnitude determiner 312 identifies the lowest value of a buffercontaining the value to be used as the integrated magnitude of thesignature. In the example implementation, a circular buffer of 12elements is sampled, with each element containing an integratedmagnitude over the corresponding time period (e.g., 7.5 seconds). Thus,the integrated magnitude determiner 312 selects the lowest value in thebuffer to yield an output to represent the integrated magnitude of thesignature(s) over, for example, the previous 90 seconds (block 710).

Example machine readable instructions 800 that may be executed toimplement the gain evaluator 402 of FIG. 4A and/or used to implement,block 504 of FIG. 5 are represented by the flowchart shown in FIG. 8.The flowchart of FIG. 8 also illustrates an example manner ofimplementing a portion of block 510 of FIG. 5. The example machinereadable instructions 800 of FIG. 8 are executed to evaluate the gainapplied to the microphone of the example audience measurement device 108to determine a fuzzy contribution value (e.g., a positive or negativevalue that corresponds to an on state or an off state) and an audio testscore value (e.g., a variable that reflects when the analysiscorresponds to an on state). While, the example machine readableinstructions 800 are shown to be executed within the example on/offidentifier 116 of the back office 114, the instructions may be executedanywhere that data collected via the audience measurement device 108 maybe accessed. For example, the example machine readable instructions 800may also be executed within the audience measurement device 108.Furthermore, the example machine readable instructions 800 may beexecuted at periodic or aperiodic intervals, based on an occurrence of apredetermined event (e.g., a full or near full memory), etc., or anycombination thereof.

The example machine readable instructions 800 operate on the audio gaindata that was collected by the microphone gain collector 302 at block502 of FIG. 5. The gain evaluator 402 then samples the audio gain of theaudience measurement device, for example, every 2 seconds (block 802).Next, the gain evaluator 402 analyzes a first sample of the gain todetermine whether the gain sample is greater than or equal to aspecified gain level (e.g., 52 dB) (block 804). If the gain evaluator402 determines that the sampled gain is greater than or equal to thespecified gain level (e.g., 52 dB), the gain evaluator 402 calculates anegative first fuzzy contribution value (block 806). For example, thefirst fuzzy contribution value associated with a gain greater than orequal to 52 dB may be calculated by the following equation: fuzzycontribution=(52−Gain)*10.

If the sampled gain is less than the specified gain level (e.g., 52 dB)(block 804), the gain evaluator 402 calculates a positive first fuzzycontribution value (block 808). For example, the first fuzzycontribution value associated with a gain less than 52 dB may becalculated by the following equation in the gain evaluator 402: fuzzycontribution=(52−Gain)*5. For positive fuzzy contribution values, thegain evaluator 402 further analyzes the first fuzzy contribution valueto determine whether the calculated first fuzzy contribution value isless than a specified limit (e.g., a limit of 90) (block 810). Forexample, if the value is less than the limit (block 810), then the fuzzycontribution value is set to the first fuzzy contribution value (block812). However, if the first calculated fuzzy contribution value isgreater than the limit (block 810), then the gain evaluator 402 sets thefuzzy contribution value to a maximum limit (block 814). The positivefirst fuzzy contribution value is limited by the gain evaluator 402 inthis manner to reduce the influence of a gain corresponding to audioinputs not associated with an audio output signal from a media device.For example, the audio gain may be low and yield a positive contributionvalue due to the example household members 110 talking within themonitored area 120 even if the monitored media device is off. In thismanner, the example machine readable instructions 800 operate to biasfirst fuzzy contribution values indicative of an off state to have agreater contribution than first fuzzy contribution values indicative ofan on state

Example machine readable instructions 900 that may be executed toimplement the remote control hint evaluator 404 of FIG. 4A and/or usedto implement block 506 of FIG. 5 are represented by the flowchart shownin FIG. 9. The example machine readable instructions 900 are executed toevaluate remote control hints corresponding to events generated from theremote control 112 to determine whether a display is in an on state oran off state. In particular, the example machine readable instructions900 are used to determine a fuzzy contribution value (e.g., a positiveor negative value that corresponds to an on state or an off state) andan audio test score value (e.g., a variable that reflects when theanalysis corresponds to an on state). While, the example machinereadable instructions 900 may be executed within the example on/offidentifier 116 of the back office 115, the instructions may be executedanywhere that data collected via the audience measurement device 108 maybe accessed. For example, the example machine readable instructions 900may be executed within the audience measurement device 108. The examplemachine readable instructions 900 may be executed at periodic oraperiodic intervals, based on an occurrence of a predetermined event(e.g., a full or near full memory), etc., or any combination thereof.

The example machine readable instructions 900 operate within the exampleremote control hint evaluator 404 upon a series of remote control hints(e.g., a series of commands entered via the remote control device 112)captured within a specified time period (e.g., thirty minutes) and aresampled around specified time intervals. For example, the hints maycomprise a series of hints fifteen minutes before and after the currentsample time and taken at 2-second intervals. The instructions of FIG. 9begin when the hints sampled within a time frame at issue are comparedby the remote control hint evaluator to determine whether two hintsoccur within a specified time (e.g., 15 minutes) of each other (block704). For example, a first hint that occurs within twelve minutes of asecond hint would satisfy this criterion. If two hints do not occurwithin the specified time, the remote control hint evaluator 404determines that the hints are not helpful in determining the on state orthe off state of the media presentation device and, therefore, thesecond fuzzy contribution value is assigned a value of zero (block 906).

If, to the contrary, the comparison at block 704 determines that twohints occurred within the specified time (e.g., 15 minutes) of oneanother, then the remote control hint evaluator 404 compares the hintsto determine whether the hints occur within the specified time of thecurrent sample times being examined (block 908). For example, if theremote control hint evaluator determines that (1) two hints occur withinfifteen minutes of each other (block 904), (2) the first hint is within15 minutes of the current sample time, but (3) the second hint occurs 18minutes before the current time (block 908), then control advances toblock 906 and the hints do not contribute to the fuzzy logic analysis.However, if the two hints occur within fifteen minutes of the currenttime (block 908), then control advances to block 910. The hints areassigned a second fuzzy contribution value of +3 (block 910).

Example machine readable instructions 1000 that may be executed toimplement the standard deviation evaluator 406 of FIG. 4A and/or used toimplement, block 514 of FIG. 5 are represented by the flowchart shown inFIG. 10. The example machine readable instructions 1000 are used todetermine the third fuzzy contribution value (e.g., a positive ornegative value that corresponds to an on state or an off state). While,the example machine readable instructions 1000 may be executed withinthe example on/off identifier 116, the instructions may be implementedanywhere that data collected via the audience measurement device 108 maybe accessed. For example, the example machine readable instructions 1000may also be executed within the audience measurement device 108.Furthermore, the example machine readable instructions 1000 may beexecuted at periodic or aperiodic intervals, based on an occurrence of apredetermined event (e.g., a full or near full memory), etc., or anycombination thereof.

The example machine readable instructions 1000 evaluate a lower boundstandard deviation (LBSD) output from the magnitude standard deviationdeterminer 314 to determine the third fuzzy contribution value to beassigned to the LBSD output (block 1004). In particular, the standarddeviation determiner calculates the third fuzzy contribution value byevaluating a function associated with the LBSD, and example being anexample function may subtract a constant from the LBSD, where theconstant value and/or function utilized in the calculation isimplementation specific and varies depending on the application. Forexample, experimental results have shown that LBSD values less than 10corresponded to an off state of the television 106 and the television onstate corresponded to LBSD values within the range of 10 to 20. Forexample, an example constant of 15, representing a threshold todetermine an on state indication. The following equation is used in theillustrated example to calculate the third fuzzy contribution value:third fuzzy contribution=LBSD−15. In this manner, the example machinereadable instructions 1000 operate to bias third fuzzy contributionvalues indicative of an off state to have a greater contribution thanthird fuzzy contribution values indicative of an on state

Example machine readable instructions 1100 that may be executed toimplement the integrated magnitude evaluator 408 of FIG. 4A and/or usedto implement block 510 of FIG. 5 are represented by the flowchart shownin FIG. 11. The example machine readable instructions 1100 evaluate theintegrated magnitude determined by the example integrated magnitudedeterminer 312 to determine whether a monitored device is in an on stateor an off state. Further, the example machine readable instructions 1100are used to determine a fuzzy contribution value (e.g., a positive ornegative value that corresponds to an on state or an off state) Whilethe example machine readable instructions 1100 are shown to be executedwithin the example on/off identifier 116, the instructions may beexecuted anywhere that data collected via the audience measurementdevice 108 may be accessed. For example, the example machine readableinstructions 1100 may be executed within the audience measurement device108. Furthermore, the example machine readable instructions 1100 may beexecuted at periodic or aperiodic intervals, based on an occurrence of apredetermined event (e.g., a full or near full memory), etc., or anycombination thereof.

The example machine readable instructions 1100 of FIG. 11 begin bycausing the integrated magnitude evaluator 408 to assign a value to thefourth fuzzy contribution value by evaluating an equation correspondingto the integrated magnitude value, for example, subtracting a constantvalue from the integrated magnitude value (e.g., an integratedmagnitude−55) (block 1102). The constant value and/or function utilizedin the calculation of the integrated magnitude value is implementationspecific and varies depending on the application. The example constantrepresents a threshold value of the integrated magnitude correspondingwith the lowest end of a range of experimentally determined values thatindicate an on state of a media presentation device. For example,experimental results from an example implementation depicted in FIG. 18demonstrate that an on state corresponds with integrated magnitudevalues in a range between +55 and +95 and an off state corresponds withintegrated magnitude values in the range between −21 and +22). In theillustrated example, the constant is 55 and the fourth fuzzycontribution value is set in accordance with the following exampleequation: fourth fuzzy contribution value=integrated magnitude−55. Theintegrated magnitude evaluator 408 then examines the fourth fuzzycontribution value to determine whether it has positive or negativevalue (block 1104). If the fourth fuzzy contribution value is a negativenumber (block 1104), then the integrated magnitude evaluator 408multiplies the fourth fuzzy contribution value by two (block 1106). Ifthe fourth fuzzy contribution value is positive (block 1104), then thefourth integrated magnitude evaluator 408 does not change the fuzzycontribution value calculated at block 1102 and the instructions of FIG.11 terminate. In this manner, the example machine readable instructions1100 operate to bias fourth fuzzy contribution values indicative of anoff state to have a greater contribution than fourth fuzzy contributionvalues indicative of an on state.

Example machine readable instructions 1200 and 1250 that may be executedto implement the example input convergence evaluator 410 of FIG. 4Aand/or used to implement block 514 of FIG. 5 are represented by theflowcharts shown in FIGS. 12A-12B. The example machine readableinstructions 1200 of FIG. 12A evaluate the number of fuzzy inputs (e.g.,fuzzy inputs corresponding to a gain applied to a microphone, remotecontrol hints, an integrated magnitude of a signature over a period oftime, a standard deviation value of a signature over a period of time,etc.) having a positive fuzzy contribution value to calculate an audiotest score value. Further, the example machine readable instructions1250 of FIG. 12B determine a fifth fuzzy contribution value (e.g., apositive or negative value that corresponds to an on state or an offstate) based on the audio test score value. While, the example machinereadable instructions 1200 and 1250 may be executed within the exampleon/off identifier 116, the instructions may be executed anywhere thatdata collected via the audience measurement device 108 may be accessed.For example, the example machine readable instructions 1200 and 1250 mayalso be executed within the audience measurement device 108.Furthermore, the example machine readable instructions 1200 and 1250 maybe executed at periodic or aperiodic intervals, based on an occurrenceof a predetermined event (e.g., a full or near full memory), etc., orany combination thereof.

The example machine readable instructions 1200 begin when the inputconvergence evaluator 410 determines if the first fuzzy contributionvalue output by the gain evaluator 402 is a value greater than zero(block 1202). If the first fuzzy contribution value is a positive number(block 1202), the output of the gain evaluator 402 indicates that themonitored media presentation device is in the on state and the audiotest score value is incremented by one (block 1204). If the first fuzzycontribution value is determined to be a negative number (block 1202),the output of the gain evaluator 402 indicates that the monitored mediapresentation device is in the off state and the audio test score valueis not incremented by the input convergence evaluator 410.

Next, the input convergence evaluator 410 evaluates, irrespective ofwhether control reached block 1206 via block 1204 or directly from block1202, the second fuzzy contribution value output by the remote controlhint evaluator 404 to determine whether the second fuzzy contributionvalue is greater than zero (block 1206). If the second fuzzycontribution value is a positive number (block 1206), the output of theremote control hint evaluator 404 indicates that the monitored mediapresentation device is in the on state and an audio test score value isincremented by one (block 1208). Control then advances to block 1210. Ifthe second fuzzy contribution value is determined to be a negativenumber (block 1206), the output of the remote control hint evaluator 404indicates that the monitored media presentation device is in the offstate and the audio test score value is not incremented by the inputconvergence evaluator 410. Control then advances to block 1210.

Irrespective of whether control reached block 1210 via block 1208 ordirectly from block 1206, the input convergence evaluator 410 thenevaluates the third fuzzy contribution value output by the standarddeviation evaluator 406 to determine whether the third fuzzycontribution value is greater than zero (block 1210). If the third fuzzycontribution value is a positive number (block 1210), the output ofstandard deviation evaluator 406 indicates that the monitored mediadevice is in the on state and an audio test score value is incrementedby one (block 1212). Control then advances to block 1214. If the thirdfuzzy contribution value is determined to be a negative number (block1210), the output of the standard deviation evaluator 406 indicates thatthe monitored media device is in the off state and the audio test scorevalue is not incremented by the input convergence evaluator 410. Controlthen advances to block 1214.

Irrespective of whether control reached block 1214 via block 1212 ordirectly from block 1210, the input convergence evaluator 410 evaluatesthe fourth fuzzy contribution value output by the integrated magnitudeevaluator 408 to determine whether the fourth fuzzy contribution valueis greater than zero (block 1214). If the fourth fuzzy contributionvalue is a positive number (block 1214), the output of integratedmagnitude evaluator 408 indicates that the monitored media device is inthe on state and an audio test score value is incremented by one (block1216). Control then advances to block 1252 of FIG. 12B. If the fourthfuzzy contribution value is determined to be a negative number (block1214), the output of the integrated magnitude evaluator 408 indicatesthat the monitored media device is in the off state and the audio testscore value is not incremented by the input convergence evaluator.Control then advances to block 1252 of FIG. 12B.

Turning to block 1252 of FIG. 12B, the input convergence evaluator 410evaluates the audio test score to assign a value to a fifth fuzzycontribution. In the illustrated example, starting at block 1252, theinput convergence evaluator 410 evaluates the audio test score todetermine if the value is zero (block 1252). The audio test score willbe zero if no input evaluators (e.g., the gain evaluator 402, the remotecontrol hint evaluator 404, the standard deviation evaluator 406 and theintegrated magnitude evaluator 408) indicated the on state. If the audiotest score is zero (block 1252), then the fifth fuzzy contribution valuefor the input convergence evaluator 410 is assigned a value of −40 bythe input convergence evaluator 410 (block 1254). The instructions ofFIG. 12B then terminate. If the audio test score is not zero (block1252), the input convergence evaluator 410 evaluates the audio testscore to determine if the audio test score is one (block 1256), theaudio test score equals one if only one of the input evaluatorsindicates the media presentation device is in the on state (block 1256).If the audio test score has a value of one (block 1256), then the inputconvergence evaluator 410 assigns a value of −30 to the fifth fuzzycontribution value (block 1258). The instructions of FIG. 12B thenterminate.

If the audio test score is not one (block 1256), the input convergenceevaluator 410 evaluates the audio test score to determine if the valueis two (block 1260). The audio test score equals two if only two of theinput evaluators indicate the media presentation device is in the onstate (block 1260). If the audio test score has a value of two, then thefifth fuzzy contribution value for the input convergence evaluator 410is assigned a value of −10 (block 1262). The instructions of FIG. 12Bthen terminate.

If the audio test score is not two (block 1260), the input convergenceevaluator 410 evaluates the audio test score to determine if the valueis three (block 1264). The audio test score equals three if only threeof the input evaluators indicate the media presentation device is in theon state (block 1264). If the audio test score has a value of three,then the fifth fuzzy contribution value for the input convergenceevaluator 410 is assigned a value of +10 (block 1266). The instructionsof FIG. 12B then terminate.

If the audio test score is not three (block 1264), the input convergenceevaluator 410 evaluates the audio test score to determine if the valueis four (block 1268). The audio test score equals four if four of theinput evaluators indicate the media presentation device is in the onstate (block 1268). If the audio test score has a value of four, thenthe fifth fuzzy contribution value for the input convergence evaluator410 is assigned a value of +30 (block 1270). The instructions of FIG.12B then terminate. However, if the audio test score is not four (block1268), the audio test score has a value outside the expected range(e.g., 0 through 4) and, therefore the audio test score is reset to 0(block 1272). The instructions of FIG. 12B then terminate.

In the illustrated example, the fifth fuzzy contribution is a valueassigned a value of −40, −30, −10, 10 or 30 depending on the value ofthe audio test score. Such assignment values are illustrative examplesand are not meant to be limiting. For example, other assignment valuesmay be used depending on the range of possible values of the audio testscore, different biases desired to be introduced to the fifth fuzzycontribution value, etc.

Example machine readable instructions 1300 that may be executed toimplement the fuzzy contribution analyzer 412 of FIG. 4A and/or used toimplement the processing at block 516 of FIG. 5 are represented by theflowchart shown in FIG. 13. The example machine readable instructions1300 are executed to analyze the fuzzy contributions provided by theabove-mentioned evaluators (e.g., the gain evaluator 402, the remotecontrol hint evaluator 404, the standard deviation evaluator 406,integrated magnitude evaluator 408 and the input convergence evaluator410). Further, the example machine readable instructions 1300 are usedto determine a sum (e.g., an intermediate fuzzy score) of all the fuzzycontribution values from the above-mentioned input evaluators. While theexample machine readable instructions 1300 may be executed within anon/off identifier (e.g., the example on/off identifier 116), a fuzzylogic engine (e.g., the fuzzy logic engine 316) and/or within ananalyzer (e.g., the fuzzy contribution analyzer 412), the instructionsmay also be executed anywhere data collected via the audiencemeasurement device 108 may be accessed. For example, the example machinereadable instructions 1300 may also be implemented within the audiencemeasurement device 108. Furthermore, the example machine readableinstructions 1300 may be executed at periodic or aperiodic intervals,based on an occurrence of a predetermined event (e.g., a full or nearfull memory), etc., or any combination thereof.

The example machine readable instructions 1300 begin, for example, whenthe fuzzy contribution analyzer 412 sums the fuzzy contribution valuesprovided by each of the example evaluators (e.g., the gain evaluator,the remote control hint evaluator 404, the standard deviation evaluator406 and the integrated magnitude evaluator 408) at the end of eachprocessing cycle (e.g., every engine cycle of two seconds) of the fuzzylogic engine 316 and stores the sum as an intermediate fuzzy score(block 1302). The fuzzy contribution analyzer 412 places theintermediate fuzzy score in a first-in, first-out (FIFO) circular bufferof, for example, 30 elements which represents data evaluated over aspecified time period (e.g., 30 engine cycles), where each elementcorresponds to one engine cycle (e.g., two seconds) (block 1304). Thefuzzy contribution analyzer 412 then determines via a timer or counterwhether a first specified time period has passed (e.g., 10-15 seconds)(block 1306). If the first time period has not passed (block 1306), thefuzzy contribution analyzer 412 determines the intermediate fuzzy scorefor the next engine cycle (block 1302). When the first specified timeperiod has passed (block 1306), the fuzzy contribution analyzer 412examines the entries in the example circular buffer using any outlierremoval method (e.g., the example method 1500 of FIG. 15) to removevalues that lie outside a specified range for valid data (e.g., betweena 25^(th) percentile and a 75^(th) percentile (block 1308). The fuzzycontribution analyzer 412 then averages the remaining values in thecircular buffer and stores the average as the final fuzzy score for thefirst time period (block 1310).

Once the final fuzzy score value is determined (block 1310), the fuzzycontribution analyzer 412 determines whether data corresponding to asecond specified time period has been collected (e.g., datacorresponding to a twenty-four hour period) (block 1312). If not,control returns to block 1302. If, however, the specified time periodhas elapsed (block 1312), the fuzzy contribution analyzer 412 examinesthe final fuzzy score values collected during the second specified timeperiod and determines the difference between the minimum and maximumvalues for the second specified time period (block 1314). The differencebetween the minimum and maximum final fuzzy score values for thespecified time period are examined to determine whether the differenceis greater than a maximum threshold value (e.g., a value of 150) (block1316). If the value is less than the threshold (1316), then the finalfuzzy score values of the hour time period are filtered (e.g., using anexample extrema filter) (block 1322). Returning to block 1316, if thedetermined difference between the minimum and maximum final fuzzy scorevalues during the second time period is greater than the threshold value(block 1316), then the fuzzy contribution analyzer 412 determines anormalization factor (block 1318). In the illustrated example, thenormalization factor is determined using the following equation:normalization factor=((((maximum value−minimum value)÷2)−maximumvalue)÷2).

After the normalization factor is computed (block 1318), the fuzzycontribution analyzer 412 adds the normalization factor to each finalfuzzy score value within the time period (block 1320). The fuzzycontribution analyzer 412 then filters the normalized fuzzy score valuesof the time period (e.g., using an example extrema filter) (block 1322).An example extrema filter may be implemented within the fuzzycontribution analyzer by determining a maximum final fuzzy score valuefor a specified number of entries (e.g., thirty entries) and thensetting the value for each of the examined entries to the determinedmaximum value.

Example machine readable instructions 1400 and 1450 that may be executedto implement the crediting contribution analyzer 414 of FIG. 4A and/orused to implement block 518 of FIG. 5 are represented by the flowchartsshown in FIGS. 14A and 14B. As shown by the example machine readableinstructions 1400 and 1450 of FIGS. 14A and 14B, the creditingcontribution analyzer 414 analyzes the final fuzzy score valuesdetermined by the fuzzy contribution analyzer 412 to determine whether amedia device was in an on state or an off state within a specified timeperiod and, thus, to determine whether media detected during the timeperiod should be credited as media exposure. While, the example machinereadable instructions 1400 may be executed within the example on/offidentifier 116, the instructions may be executed anywhere data collectedvia the audience measurement device 108 may be accessed. For example,the example machine readable instructions 1400 may also be implementedwithin the audience measurement device 108. Furthermore, the examplemachine readable instructions 1400 may be executed at periodic oraperiodic intervals, based on an occurrence of a predetermined event(e.g., a full or near full memory), etc., or any combination thereof.

The example machine readable instructions 1400 begin when the creditingcontribution analyzer 414 extracts final fuzzy score valuescorresponding to particular time periods (e.g., 10 or 15 secondintervals beginning at a certain specified time) (block 1402). Thecrediting contribution analyzer 414 then analyzes signature matchingdata and/or crediting information corresponding to the same example timeperiod to determine whether a match (e.g., a signature match and/orcrediting match) was found within the specified time period (block1404). If the crediting contribution analyzer 414 determines a signaturematch occurred during the examined time period, each final fuzzy scorewithin the examined time period is adjusted by a specified value (e.g.,adding a constant value of +125) (block 1406). Conversely, if asignature match was not determined in the examined time period, eachfinal fuzzy score e within the time period is adjusted by a secondspecified value (e.g., a constant value of −125) (block 1408). The firstand second specified values used to adjust the final fuzzy score may beconstant values, as in the illustrated example, and/or determined basedon an equation corresponding to the match. The constant value and/orequation utilized by the crediting contribution analyzer 414 toincrement the final fuzzy score is implementation specific and variesdepending on the application.

Next, the crediting contribution analyzer 414 determines whether allfinal fuzzy scores have been evaluated (block 1410). If all of the finalfuzzy scores have not been evaluated, the crediting contributionanalyzer 414 extracts the final fuzzy scores for the next time period tobe examined (block 1402). If the crediting contribution analyzer 414 hasexamined and adjusted all of the final fuzzy scores for the current timeperiod, the adjusted final fuzzy score values are processed by anextrema filter to determine time intervals during which a mediapresentation device may have been in an on state or an off state (block1412). For brevity, an interested reader is referred to the exampleextrema filter discussed above in conjunction with FIG. 13.

Turning to FIG. 14B, the example machine readable instructions 1450begin when the creditor 416 extracts a timestamp associated with a finalfuzzy score value associated with a start time of the specified timeperiod (block 1452). The creditor 416 then collects media exposureinformation, including both the signature and any crediting match, foundfor the specified time period within a database in the back office 116(block 1454). The creditor 418 then reviews the timestamp associatedwith the collected media exposure (block 1456). Next, the creditor 416gathers the final fuzzy score value corresponding to the timestampassociated with the crediting information (block 1458).

The timestamps associated with the media exposure and the timestampassociated with the final fuzzy value are then analyzed to determinewhether the information presentation device was on at the time specifiedby the associated timestamps (block 1460). If the creditor 416determines that the media presentation device was on (block 1460), thenthe media exposure information is not modified and processing continuesuntil the last of the media exposure data corresponding to the specifiedtime period has been examined (block 1468). Conversely, if the mediapresentation device was determined to be off by the creditor 416 (block1460), then the media exposure information associated to the timestampis marked to indicate that no valid crediting match occurred during thetime (block 1466). Once the creditor 416 marks the exposure, the mediaexposure information is examined to determine whether the last of themedia exposure data had been examined (block 1468). If the creditor 416determines that no more media exposure information remains, theinstructions of FIG. 14B terminate. Conversely, if the creditor 418determines that more media exposure information remains (block 1468),then the instructions return to gather the next media exposureinformation (block 1454).

Example machine readable instructions 1500 that may be executed toidentify data points falling outside a specified range of values withinan examined time period (e.g., outliers) are represented by theflowchart shown in FIG. 15. The example instructions of FIG. 15 may beused to implement, for example, the fuzzy contribution analyzer 412and/or block 1308 of FIG. 13. The example machine readable instructions1500 determine a data point that lies outside a specified range ofvalues of a data set (e.g., outside the range between a first quartileand a third quartile). While, the example machine readable instructions1500 of FIG. 15 may be executed by the example on/off identifier 116,the instructions may be executed anywhere that data collected via theaudience measurement device 108 may be accessed. For example, theexample machine readable instructions 1500 may also be implementedwithin the audience measurement device 108. Furthermore, the examplemachine readable instructions 1500 may be executed at periodic oraperiodic intervals, based on an occurrence of a predetermined event(e.g., a full or near full memory), etc., or any combination thereof.

The example machine readable instructions 1500 begin by ordering thedata within the set to be examined (e.g., the data stored within thebuffer as explained in conjunction with the instructions 1300 describedabove) from the smallest to largest value (e.g., an example of anordered data set comprising nine entries is: 35, 47, 48, 50, 51, 53, 54,70, 75) (block 1502). Next, the range containing valid data isdetermined by calculating indexes associated with the start and end ofthe valid data range (e.g., calculating an index associated with the25^(th) percentile or first quartile and an index associated with the75^(th) percentile or third quartile of the examined values) (block1504). A percentile value is determined by multiplying the sample size(e.g., the number of values to be examined) by the percentile to becalculated (e.g., the 25^(th) or Q1 value and the 75^(th) percentile orQ3 value) (block 1504). Once the fuzzy contribution analyzer 412, forexample, determines the percentiles (e.g., the 25^(th) and 75^(th)percentiles corresponding to the first quartile and third quartiles,respectively) an interquartile range is determined for use incalculating constructing a lower and an upper fence for evaluating thedata (block 1506). For example, a 25^(th) percentile for a series ofnine numbers may be calculated by the following: 9*0.25=2.25. If apercentile calculated is not an integer index, the index is rounded upto the next integer, so in the preceding example a 25^(th) percentileindex would be correspond to the 3^(rd) element in the ordered list(e.g., Q1=48). Similarly, the 75^(th) percentile value would correspondto the seventh ordered element (e.g., Q3=54).

Once the percentile indexes are calculated, an upper fence value and alower fence value are determined for use in determining outliers (block1508). A value within a sampled data set is termed an outlier if it liesoutside a determined, so-called fence. The lower fence values may becalculated by the following equations, where Q1=the 25^(th) percentiledata value itself and Q3=the 75^(th) percentile data value itself (block1508). The lower fence value is determined by Q1−1.5*(Q3−Q1) and theupper fence value may be calculated by Q3+1.5*(Q3−Q1) (block 1508). Forthe above example data set, the lower fence is calculated to be48−1.5*(54−48)=39 and the upper fence value is calculated to be54+1.5*(54−48)=63. Once the crediting contribution analyzer 314determines the upper and lower fence values, the outliers are identifiedas the values above the upper fence and below the lower fence andeliminated (block 1510). Any value of the example data set that fallsoutside the range of 39 through 63, is determined to be an outlier(e.g., in the example data set, the values 35, 70 and 75 are outliers).

FIG. 16 is a graph representing example gain levels 1602 of a microphoneassociated with an audience measurement device 108 of FIG. 1 versus time1604. The gain levels 1602 may be obtained from the data file 204 ofFIG. 2) by the gain collector 304 and processed by the gain evaluator402 as explained above. Periods of time, such as the example time period1606, are further labeled to indicate the actual operating state (e.g.,an on state or an off state) of a media presentation device (e.g., theSTB 104 and/or the television 106) during the respective period of time1606 measurement was performed by the audience measurement device 108.

As described above in conjunction with the gain evaluator 402, a gainthreshold 1608 (e.g., 52 dB) is defined as the threshold used todetermine whether the captured gain (e.g., the gain level 1610)generates a positive fuzzy contribution value (e.g., corresponding to alikely on state) or a negative fuzzy contribution value 1612 (e.g.,corresponding to a likely off state). In the illustrated example, a gainlevel below the threshold correspondingly yields a positive fuzzy valueand a gain level below the threshold yields a negative fuzzy value.However, a gain level 1614 having a value above the threshold 1608(e.g., 55 dB>52 dB) may occur even when the monitored device is in an onstate. This condition may correspond to a low volume audio output or amute state of the media presentation device. Conversely, a gain level1610 associated with an off state of the monitored device may have avalue below the threshold 1608 as a result of persons (e.g., thehousehold members 110) speaking within the metering area 120.

FIG. 17 depicts example standard deviation data calculated by themagnitude standard deviation determiner 314 and graphed versus time. Anexamination of these experimental results reveals that standarddeviations value between zero and ten (the standard deviation value1702) are associated with a television off condition. Further, theexperimental results also revealed that an on state correlated with astandard deviation value within the range of standard deviation values1704 between 10 and 20. Also, a very high standard deviation, forexample the standard deviation value 1706, also was associated with anoff condition and may, thus, also be included in the calculation of afuzzy contribution value within a the standard deviation evaluator 406of FIG. 4A.

FIG. 18 is a graph depicting example magnitude values that may begenerated by the integrated magnitude determiner 312. The sample outputscorrespond to the integrated magnitude of a signature associated with anaudio signal captured by a microphone of the audience measurement device108. As discussed above in conjunction with the integrated magnitudedeterminer 312 of FIG. 3 and with FIG. 7, the integrated magnitude valueonly generates a fuzzy contribution value when the integrated magnitudevalue is determined to be negative (e.g., the data point 1802) becausevalues above zero may be associated with either an on state or an offstate. A negative magnitude value may be due to, for example, a changein gain of the audio signal used to create the signature or a change in,or occurring during, a normalization process for the signature.

FIGS. 19A and 19B are figures representing example intermediate fuzzyscore values that may be calculated by the fuzzy logic engine 316. FIG.19C is a figure representing example final fuzzy score values that maybe calculated by the fuzzy logic engine 316. For example, FIGS. 19A andB represent an example intermediate fuzzy score value that has beencalculated in the fuzzy contribution analyzer 412 as discussed inconjunction with FIG. 4A and with FIG. 13. FIG. 19A also represents anintermediate fuzzy score record that may be determined prior to thenormalization procedure discussed above along with FIG. 13. Theseexperimental results indicate that a fuzzy score record may becomecentered on a number much less than zero. For example, an intermediatefuzzy value (e.g., the data point 1902) indicating an on state may havea value of +10, and another intermediate fuzzy value (e.g., the datapoint 1904) that represents an off state may have a value of −200. Forconsistency, the intermediate fuzzy values included in the fuzzy scorerecord are preferably centered on zero, so that any positive value isassociated with an on state and a negative value is associated with anoff state.

FIG. 19B represents the example intermediate fuzzy score value of FIG.19A after application of a normalization method to the data to centerthe intermediate fuzzy values resenting an on state and off state aroundzero (e.g., the normalization procedure described above in conjunctionwith FIG. 13).

Finally, FIG. 19C represents example final fuzzy score valuescorresponding to the intermediate fuzzy values shown in FIG. 19A andnormalized as shown in FIG. 19B. Additionally, the final fuzzy scoresshown in FIG. 19C reflect adjustment by signature matching contributiondetermined from processing in the crediting contribution analyzer 414.As shown in FIG. 19C, the crediting and/or signature match contributionmakes a significant impact on the output of the fuzzy logic engine 316.As shown, the crediting and/or signature match contribution can enhancethe fuzzy score to differentiate between fuzzy scores representingwhether a media presentation device is in an on state or an off state.

Moving to FIG. 20, this graph represents an example final fuzzy scoreoutput from the fuzzy logic engine 316 that can be used to determinetime periods where a media presentation device (e.g., the exampletelevision 106) was in an on state or in an off state and is shown bythe signal 2002. The fuzzy logic engine 316 is configured to output apositive value to represent when a media presentation device is in an onstate and a negative value to represent when a media presentation deviceis in an off state. The actual operating state of a media presentationdevice during a monitored time period can be compared with the exampleoutput signal 2002 by referring to the actual operating states 2004.

Further, the range between the representations of on state and off statevalues was extended to allow the fuzzy score to experience variationswithout affecting the overall score, as seen in areas 2006 and 2008. Therange extension was implemented, for example, by utilizing the inputconvergence evaluator 410 discussed above in conjunction with FIG. 4A todetermine a fifth fuzzy contribution value representative of the numberof input evaluators (e.g., the gain evaluator 402, the remote controlhint evaluator 404, the standard deviation evaluator 406, and theintegrated magnitude evaluator 408) that indicated an on state (i.e. hada positive fuzzy contribution score). Additionally, the range wasextended by utilizing an adjustment value implemented as a step inputbased on a signature matching contribution factor (e.g., the adjustmentof the crediting contribution analyzer 414).

FIG. 21 is a schematic diagram of an example processor platform 2100that may be used and/or programmed to execute any or all of the examplemachine readable instructions of FIGS. 5-15 to implement the on/offidentifier 116, the remote control hint collector 302, the microphonegain collector 304, the data collector 306, the signature collector 308,the signature characteristic determiner 310, the integrated magnitudedeterminer 312, the magnitude standard deviation determiner 314, thefuzzy logic engine 316, the output database 318, the gain evaluator 402,the remote control hint evaluator 404, the standard deviation evaluator406, the integrated magnitude evaluator 408, the input convergenceevaluator 410, the fuzzy contribution analyzer 412, creditingcontribution analyzer 414, and/or the creditor 418 of FIGS. 1-4A. Forexample, the processor platform 2100 can be implemented by one or moregeneral-purpose processors, microcontrollers, etc. The processorplatform 2100 of the example of FIG. 21 includes at least onegeneral-purpose programmable processor 2102. The processor 2102 executescoded instructions 2104 and/or 2106 present in main memory of theprocessor 2102 (e.g., within a RAM 2108 and/or a ROM 2110). Theprocessor 2102 may be any type of processing unit, such as a processoror a microcontroller. The processor 2102 may execute, among otherthings, the example methods and apparatus described herein.

The processor 2102 is in communication with the main memory (including aRAM 2108 and/or a ROM 2110) via a bus 2112. The RAM 2108 may beimplemented by dynamic random-access memory (DRAM), synchronous dynamicrandom-access memory (SDRAM), and/or any other type of RAM device, andthe ROM 2110 may be implemented by flash memory and/or any other desiredtype of memory device. A memory controller 2114 may control access tothe memory 2108 and the memory 2110. In an example implementation, themain memory (e.g., RAM 2108 and/or ROM 2110) may implement the exampledatabase 204 of FIG. 2.

The processor platform 2102 also includes an interface circuit 2116. Theinterface circuit 2116 may be implemented by any type of interfacestandard, such as an external memory interface, serial port, generalpurpose input/output, etc. One or more input devices 2118 and one ormore output devices 2120 are connected to the interface circuit 2116.

Although certain example methods, apparatus and articles of manufacturehave been described herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe appended claims either literally or under the doctrine ofequivalents.

1. A method for determining whether a media presentation device is in anon state or an off state, the method comprising: determining first andsecond characteristics of a signature associated with a signalrepresentative of media content presented via the media presentationdevice; evaluating the first and second characteristics to determinefirst and second fuzzy contribution values, the first fuzzy contributionvalue representing a degree with which the first characteristiccorresponds to the media presentation device being in at least one ofthe on state or the off state, the second fuzzy contribution valuerepresenting a degree with which the second characteristic correspondsto the media presentation device being in at least one of the on stateor the off state; determining an audio test score based on a number ofthe first and second contribution values that indicate the mediapresentation device is in one of the on state or the off state;converting the audio test score into a third fuzzy contribution value;combining the first, second and third fuzzy contribution values tocalculate an intermediate fuzzy score; and determining whether the mediapresentation device is in the on state or the off state based on theintermediate fuzzy score.
 2. A method as defined in claim 1, wherein thesignal comprises an audio signal associated with the media content, theaudio signal being collected by an audience measurement device.
 3. Amethod as defined in claim 1, further comprising: calculating a fourthfuzzy contribution value based on a gain applied to a microphone of anaudience measurement device that detected the signal representative ofthe media content; wherein combining the first, second and third fuzzycontribution values to calculate the intermediate fuzzy score furthercomprises combining the first, second, third and fourth fuzzycontribution values to calculate the intermediate fuzzy score.
 4. Amethod as defined in claim 3 wherein the fourth fuzzy contribution iscalculated by subtracting the gain from a first constant to form a firstdifference and multiplying the first difference by a second constant. 5.A method as defined in claim 1, wherein determining the firstcharacteristic of the signature further comprises integrating amagnitude associated with the signature over a period of time.
 6. Amethod as defined in claim 1, wherein determining the secondcharacteristic of the signature further comprises determining a standarddeviation of a magnitude associated with the signature over a period oftime.
 7. A method as defined in claim 1, further comprising calculatinga fifth fuzzy contribution value based on a hint from a remote controldevice associated with the media presentation device, wherein combiningthe first, second and third fuzzy contribution values to calculate theintermediate fuzzy score further comprises combining the first, second,third and fifth fuzzy contribution values to calculate the intermediatefuzzy score.
 8. A method as defined in claim 1, wherein the intermediatefuzzy score comprises a first intermediate fuzzy score, and furthercomprising averaging the first intermediate fuzzy score with a secondintermediate fuzzy score to calculate a first final fuzzy score for atime period associated with the first and second intermediate fuzzyscores.
 9. A method as defined in claim 8, further comprising at leastone of normalizing or filtering the first final fuzzy score.
 10. Amethod as defined in claim 8, further comprising adjusting the firstfinal fuzzy score by a first amount if the signature matched a referencesignature in a database and adjusting the first final fuzzy score by asecond amount if the signature did not match a reference signature inthe database.
 11. A method as defined in claim 10, wherein the firstamount and the second amount have different polarities.
 12. A method asdefined in claim 8, wherein determining whether the media presentationdevice is in the on state or the off state based on the intermediatefuzzy score comprises determining whether the media presentation deviceis in the on state or the off state based on the first final fuzzyscore.
 13. An apparatus for determining whether a media presentationdevice is in an on state or an off state, the apparatus comprising: asignal characteristic determiner to determine first and secondcharacteristics of a signature associated with a signal representativeof media content presented via the media presentation device; a firstevaluator to evaluate the first characteristic to determine a firstfuzzy contribution value, the first fuzzy contribution valuerepresenting a degree to which the first characteristic corresponds tothe media presentation device being in at least one of the on state orthe off state; a second evaluator to evaluate the second characteristicto determine a second fuzzy contribution value, the second fuzzycontribution value representing a degree to which the secondcharacteristic corresponds to the media presentation device being in atleast one of the on state or the off state; an input convergenceevaluator to determine an audio test score based on a number of thefirst and second contribution values that indicate the mediapresentation device is in one of the on state or the off state, and toconvert the audio test score into a third fuzzy contribution value; afuzzy contribution analyzer to combine the first, second and third fuzzycontribution values to calculate an intermediate fuzzy score; and acreditor to determine whether the media presentation device is in the onstate or the off state based on the intermediate fuzzy score.
 14. Anapparatus as defined in claim 13, wherein the signal representative ofthe media content is an audio signal associated with the media content,the audio signal being collected by an audience measurement device. 15.An apparatus as defined in claim 13, wherein the third fuzzycontribution is set to a first constant when none of the first andsecond fuzzy contributions indicate that the media presentation deviceis in the on state, the third fuzzy contribution is set to a secondconstant when one of the first and second fuzzy contributions indicatethat the media presentation device is in the on state, and the thirdfuzzy contribution is set to a third constant when both of the first andsecond fuzzy contributions indicate that the media presentation deviceis in the on state.
 16. An apparatus as defined in claim 13, furthercomprising a gain evaluator to calculate a fourth fuzzy contributionvalue based on a gain applied to a microphone of an audience measurementdevice that detected the signal representative of the media content;wherein the fuzzy contribution analyzer is to combine the first, second,third and fourth fuzzy contribution values to calculate the intermediatefuzzy score.
 17. An apparatus as defined in claim 16, wherein the fourthfuzzy contribution is calculated by subtracting the gain from a firstconstant to form a first difference and multiplying the first differenceby a second constant.
 18. An apparatus as defined in claim 13, whereinthe first evaluator determines the first fuzzy contribution value byintegrating a magnitude associated with the signature over a period oftime.
 19. An apparatus as defined in claim 18, wherein the firstevaluator determines the first fuzzy contribution by subtracting a firstconstant from the integrated magnitude to form a difference.
 20. Anapparatus as defined in claim 19, wherein, if the difference has anegative polarity, the first evaluator multiplies the difference with asecond constant.
 21. An apparatus as defined in claim 13, wherein thesecond evaluator determines the second fuzzy contribution value bydetermining a standard deviation of a magnitude associated with thesignature over a period of time.
 22. An apparatus as defined in claim21, wherein the second evaluator subtracts a constant from the standarddeviation of the magnitude.
 23. An apparatus as defined in claim 13,further comprising a remote control hint evaluator to calculate a fifthfuzzy contribution value based on a hint from a remote control deviceassociated with the media presentation device, wherein the fuzzycontribution analyzer is to combine the first, second, third and fifthfuzzy contribution values to calculate the intermediate fuzzy score. 24.An apparatus as defined in claim 13, wherein the intermediate fuzzyscore comprises a first intermediate fuzzy score, and wherein the fuzzycontribution analyzer averages the first intermediate fuzzy score with asecond intermediate fuzzy score to calculate a first final fuzzy scorefor a time period associated with the first and second intermediatefuzzy scores.
 25. An apparatus as defined in claim 24, wherein the fuzzycontribution analyzer is to at least one of normalize or filter thefirst final fuzzy score.
 26. An apparatus as defined in claim 24,further comprising a crediting contribution analyzer to adjust the firstfinal fuzzy score by a first amount if the signature matched a referencesignature in a database and to adjust the first final fuzzy score by asecond amount if the signature did not match a reference signature inthe database.
 27. An apparatus as defined in claim 26, wherein the firstamount and the second amount have different polarities.
 28. An apparatusas defined in claim 13, wherein the creditor determines whether themedia presentation device is in the on state or the off state based onthe first final fuzzy score.
 29. A machine readable medium storingmachine readable instructions that, when executed, cause a machine to:determine first and second characteristics of a signature associatedwith a signal representative of media content presented via a mediapresentation device; evaluate the first and second characteristics todetermine first and second fuzzy contribution values, the first fuzzycontribution value representing a degree with which the firstcharacteristic corresponds to the media presentation device being in atleast one of an on state or an off state, the second fuzzy contributionvalue representing a degree with which the second characteristiccorresponds to the media presentation device being in at least one ofthe on state or the off state; determine an audio test score based on anumber of the first and second contribution values that indicate themedia presentation device is in one of the on state or the off state;convert the audio test score into a third fuzzy contribution value;combine the first, second and third fuzzy contribution values tocalculate an intermediate fuzzy score; and determine whether the mediapresentation device is in the on state or the off state based on theintermediate fuzzy score.
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